{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capital Bikeshare数据探索\n",
    "\n",
    "请在 Capital Bikeshare （美国 Washington, D.C.的一个共享单车公司）提供的自行车数据上进行回归分析。训练数据为 2011 年的数据，要求预测 2012 年每天的单车共享数量。\n",
    "\n",
    "1)  训练数据和测试数据分割（请将 2012 年的数据作为测试数据）；（20 分）\n",
    "2)  适当的特征工程（及数据探索）;（20 分）\n",
    "    提示：\n",
    "    a)  有些特征看起来是数据值特征，其实是类别型特征，如月份、季节\n",
    "    b)  数值型特征归一化\n",
    "    c)  可以丢弃一些不必要的特征\n",
    "3)  岭回归，并选择最佳的正则参数；（30 分）\n",
    "    a)  参数调优\n",
    "    b)  结果可视化\n",
    "4)  Lasso，并选择最佳的正则参数；（30 分）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "from sklearn.metrics import r2_score  #评价回归预测模型的性能\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# path to where the data lies\n",
    "#dpath = './data/'\n",
    "data = pd.read_csv(\"bikeshare_day.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据基本信息\n",
    "\n",
    "- 训练样本数目N：731个样本 （数据规模不大）\n",
    "- 输入数据属性x：13个特性\n",
    "- 输出属性y：3个，非注册用户数、注册用户数、日租车人次（属性皆为连续值，所以为回归问题）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 数据没有缺失值，所以不用考虑数据填补"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0\n",
       "dteday        0\n",
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 查看是否有空值\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 探索数据\n",
    "\n",
    "- instant 记录号，（可以忽略）\n",
    "- season 季节， "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 各属性的统计特性\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 单变量分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8340748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 目标y registered的直方图／分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.registered.values, bins=30, kde=True)\n",
    "plt.xlabel('registered value of Bikeshare', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6dbbb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "plt.scatter(range(data.shape[0]), data[\"registered\"].values,color='purple')\n",
    "plt.title(\"Distribution of registered\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hGtq6gg5HRNCDi0atIw3tAEzJHpp7CoK8T+Jk3bRoGj9Ze5D/ee0Qt15UGnQ4IuOezihGqSMN42sgO9JZRVmcU5TF42v2480EIyJBUqIYpQ7XtzMhOUxGyvg86bvh/BJ2VDazqULzP4kETYlilPIGslPwnv80/lx7zmSSwyF+uuHg4JVFZFgpUYxC3T29VDW1j7uB7EiZKYksOXMSK18/rDu1RQKmRDEKVTZ20OvGzx3ZA7n+vGIa27t5fmtl0KGIjGtKFKPQ2wPZ43sW1Ytm5DE1O5WfbqgIOhSRcW18jpSOcpWN7SQmGDnpwzu1+GgR7dLdMyZm8PvtVXz397vJSk3st85Ni0uGKzQRQWcUo1JlUweFGSmExulAdqSFJdk44LUDeqa2SFCUKEahysZ2JmYmBx3GqJA3IZnSvHQ27K/TPRUiAVGiGGVaO7tpau9mop7y9pbzpuVQ09LJgdrWoEMRGZeUKEaZysYOAAozlCiOO3NqJkkJITbsV/eTSBCUKEaZykZvjid1Pb0tOZzAWVOz2HSogc7u3qDDERl3lChGmcrGdpLDoQGv8BmvFk7LobO7ly2HNaWHyEhTohhlqpo6mJg5fqfuGEhpXhq56UnqfhIJgBLFKOKc0xVPAzAzFpZks+dYC3WtnUGHIzKuKFGMIs0d3bR29uiKpwEsKM4B4LUDekyqyEhSohhFjl/xpETRv5z0JMry03ntgO6pEBlJShSjyPErngoz1PU0kIUluqdCZKQpUYwilY3tpCUlMCFZU3AN5MwpmSQmGK+q+0lkxChRjCK64mlwyYkJnDklizcO1dPVo3sqREaCEsUooSueYregJIf2rl62HWkMOhSRcUGJYpRoaOuio7tXA9kxmF6QTlZqIq9qRlmREaFEMUq8dcWT5ngaVMiMBcXZ7KxsprG9K+hwRMY8JYpR4u05npQoYrGgJAcHvK5BbZFhF1OiMLMlZrbdzHaZ2d39rE82syf99WvMrDRi3T1++XYzu+ok2vymmTWf2m7Fn8rGdjJTwqQmJQQdSlwoyEimOCeVV3VPhciwGzRRmFkC8G3gamAecKOZzetT7Q6gzjk3E3gAuN/fdh6wDJgPLAG+Y2YJg7VpZuVA9mnuW1w5fsWTxG7htByqmjrYfEiD2iLDKZYzikXALufcHudcJ7AcWNqnzlLgh/7yU8CV5l3juRRY7pzrcM7tBXb57Q3Ypp9E/gP4x9PbtfjR0+uoamrXjXYn6eyp2YRDxk83HAw6FJExLZZEMRWI/J9Y4Zf1W8c51w00AHlRto3W5l3ASufckdh2If4drG2lq8fpjOIkpSYlMH9KJj9/7RDtXT1BhyMyZsWSKPq7+6tvp/BAdU6q3MymANcD3xw0KLM7zWy9ma2vrq4erPqotqOyCdBA9qkoL82lqb2b32w+GnQoImNWLImiAiiOeF0EHB6ojpmFgSygNsq2A5UvAGYCu8xsH5BmZrv6C8o595Bzrtw5V15QUBDDboxexxNFoW62O2ll+elMy0tj+boDQYciMmbFkijWAbPMrMzMkvAGp1f2qbMSuNVf/jDwkvMuRVkJLPOviioDZgFrB2rTOfdr59wk51ypc64UaPUHyMe07ZXN5KQlkhzWFU8nK2TGX5QXs3pPLfuOtQQdjsiYNGii8Mcc7gKeA7YBK5xzW8zsXjO7zq/2CJDnf/v/HHC3v+0WYAWwFfgN8GnnXM9AbQ7trsWPnZVN6nY6DR8+r4iQwYr1GtQWGQ4xTVPqnHsGeKZP2Rcjltvxxhb62/Y+4L5Y2uynzoRY4otnXT297K5u5sLp+UGHErcmZqZwxZxCVqyv4G/ecwZJYd1HKjKU9D8qYPuOtfhXPGl84nTcfME0jjV38NwWDWqLDDUlioDtqPRuPlfX0+m5bFYBxbmp/Hj1/qBDERlzlCgCtr2yiZB5U1LIqQuFjFsWT2Pt3lq2H20KOhyRMUWJImA7jjZRmpdOYoL+FKfr+vJiksIhfrx6X9ChiIwp+nQK2I7KJs6YmBF0GGNCbnoS1549mZ+/eogmTT8uMmSUKALU3tXDvpoWzpg45i/uGjG3XlhKS2cPK9ZXBB2KyJihRBGg3dXN9Do4Y5LOKIbKOcXZLCrN5dFX9tKtZ2qLDAkligAdn7pjtrqehtQnLp3Oofo2ntH8TyJDQokiQDsqm0lMMErz04MOZUy5ck4h0wvSeejl3XqokcgQUKII0I6jTUzPn6ArnoZYKGR84l3T2XyokdV7aoMORyTuxTSFhwyP7ZVNLCjJCTqMuPfEmhNnju3q6SU9OcwXf7GZ5z93WQBRiYwd+iobkJaObirq2jijUFc8DYfEhBCXzspnZ1Uz6/fprELkdChRBGRnlTd1h654Gj6Ly/JITw7zwAs7gg5FJK4pUQRkx1Fd8TTcksIhLpuVz6pdNazZUxN0OCJxS4kiINsrm0gOhyjOTQs6lDFtUVke+ROSeeCFHboCSuQUKVEE5M2jjcyelEFCqL/Hh8tQSQqH+PS7Z7B6Ty2/3x7fz1YXCYoSRQCcc2w70sTcSZlBhzIu3Lx4GtPz0/mXX2+lS3dri5w0JYoAVDV1UNvSydzJGp8YCUnhEP/0/rnsqW7hMT2vQuSkKVEEYOuRRgDmTtYZxUi5Yk4h75qVz3+9sJO6ls6gwxGJK0oUAdjmJ4o5ShQjxsz4wrXzaO7o5ivPbgs6HJG4okQRgG1HmpianUpWamLQoYwrZ0zM4M5Lp7NifQWv7DwWdDgicUOJIgBvHmnU+ERAPnvlLKbnp3P305to7ewOOhyRuKBEMcLau3rYc6xF4xMBSUlM4Kt/fjYVdW38+2+2Bx2OSFxQohhhOyub6el1ShQBWlSWy20XlfKDP+7jd9urgg5HZNRTohhh23TF06hw99VzmDMpg79fsZGqxvagwxEZ1ZQoRtjWI42kJiZQoqk7ApWSmMC3blpAS2c3f7vidXp6Nb2HyECUKEaYpu4YPWYWZvDl6+azalcNX3te4xUiA9GDi0bQ8ak7rjlrctChjCv9PdjoOOccy84v5tu/283cyZlce/aUmLcFuGlxyZDEKDKa6YxiBB2obaWhrYuzpmYFHYr4zIwvL53PedNy+IefbmLL4YagQxIZdZQoRtDGCu9D6OwiJYrRJDmcwHdvWUh2WiIf+8E6DtW3BR2SyKiiRDGC3qioJykcYraeajfqFGak8P3bz6e1o4dbH11LfavmgxI5ToliBG2saGDe5EwSE3TYR6M5kzJ56KPlHKhp5eM/XE97V0/QIYmMCjF9YpnZEjPbbma7zOzuftYnm9mT/vo1ZlYase4ev3y7mV01WJtm9rhfvtnMHjWzMTEhUk+vY/OhBs5Rt9OoduGMPL52wzlsOFDHZ5e/Rq+eiicyeKIwswTg28DVwDzgRjOb16faHUCdc24m8ABwv7/tPGAZMB9YAnzHzBIGafNxYA5wFpAKfPy09nCU2F3dTGtnD2cXZQcdigzi2rOn8IX3z+O5LZX8cuNhPUJVxr1YzigWAbucc3ucc53AcmBpnzpLgR/6y08BV5qZ+eXLnXMdzrm9wC6/vQHbdM4943zAWqDo9HZxdNikgey48rFLyrjz0ums2VuraT5k3IslUUwFDka8rvDL+q3jnOsGGoC8KNsO2qbf5fQR4DcxxDjqbaqoJz0pgekFE4IORWJ095I5LCjO5oVtVfxxt6Yll/Erlhvu+ruFuO+5+EB1BirvL0H1bfM7wMvOuT/0G5TZncCdACUlo/+mp40VDZw5NUt3ZMeRUMj40MIiOrp7+dWmI6QkJrCwJCfosERGXCyJogIojnhdBBweoE6FmYWBLKB2kG0HbNPM/hkoAD45UFDOuYeAhwDKy8tHdSdyZ3cv2w43ctvFpUGHIv2Idvd1Qsi44fxifvSnfTz9agUp4RDzpqj7UMaXWLqe1gGzzKzMzJLwBqdX9qmzErjVX/4w8JI/xrASWOZfFVUGzMIbdxiwTTP7OHAVcKNzrvf0dm902FHZRGdPr+7IjlOJCSFuuWAaU7NT+cm6g+yqag46JJERNWii8Mcc7gKeA7YBK5xzW8zsXjO7zq/2CJBnZruAzwF3+9tuAVYAW/HGGj7tnOsZqE2/rQeBicCfzOx1M/viEO1rYF47UAfAucW64ileJYcTuPWiUvInJPHY6v0cqG0NOiSRERPTpIDOuWeAZ/qUfTFiuR24foBt7wPui6VNv3zMTVS4Zm8tk7NSKMpJDToUOQ1pSWFuv7iMh17eww//uI9PvGt60CGJjAjdIjzMnHOs3VvLorJcvCuGJZ5lpiRyx8VlJCYY31+1l33HWoIOSWTYKVEMs301rVQ1dbCoLDfoUGSI5KQncfvFZfQ4xy2PrOFog56QJ2ObEsUwW7u3BoDFShRjysTMFG67qJT61i5ueWQNtS2aRFDGLiWKYbZmby156UnM0I12Y05RThrfu7Wcg7Wt3ProWhpau4IOSWRYjLmB49FG4xNj2wXT83jwlvP45I83cPMjq3nsjsVkpyUFHdZb9IQ+GQo6oxhGh+rbqKhr0/jEGPfuOYX890fOY0dlMzc9rG4oGXuUKIbRur21AEoU48C75xTy8EfL2V3dzE0Pr6amuSPokESGjBLFMFqzt5aMlDBzJmUGHYqMgMvOKOCRW89nX00LNz68muomJQsZG5Qoholzjpd3VLO4LE8TAY4jl8zK59HbzudgbRt/8d9/Yn+N7rOQ+KdEMUx2VDZzqL6NK+cWBh2KjLCLZuTz2McXUd/ayQe/80c27K8LOiSR06JEMUxe2FYJwBVzlCjGo/Om5fL0X11MRkqYGx9ezYp1BwffSGSU0uWxw+TFbZWcNTWLiZkpQYciASnLT+fmxdN4ct0B/vFnm1i+7iDXnTOFpPDb38/i+fJUXXo7fuiMYhgca+7gtYP16nYSJiR7Ewm+e3Yhrx2o45sv7WTPMU1TLvFFiWIY/O7NKpyD98ydGHQoMgqEzHjvvIl87JIyHPC9P+zl6VcraGzXndwSH9T1NAxe3FbFpMwU5k/RZbHythkFE/jMFbN4YVslf9x9jI0V9dS1dPKxS8qGpYuyqb2LvcdaONbUQXVzBw1tXTS1d9HS2UN3Ty/dPY5vvLiTpHCI9OQwEzOTmZiRwsSsFCZmJlOck8asiROYlJmimQXGOSWKIdbR3cMfdlazdMFU/eeSEySFQ1xz1mQumJ7Hi9sqeegPe/jeK3u5ck4hH1pYxCWz8pmQfPL/LWtbOnnzaCNbDjXyxqEGNh9qYE/EFOjhkJGdlkhGSiITM5JJTAgRTjCm50+go7uHxvZuqpra2XK4kWPNHbiIhwtnJIeZOXECswoncObULBaW5DB7UsZQHA6JE0oUQ+zFbVW0dPZw1fxJQYciI2CwAd2B5KYncX15MQ/ccC4/WXeAp9ZX8NutlSQmGAtKcpg/JZM5kzKYlJVKdmoiaUkJdPU4Ont6qWnuoKqpg33HWth2tIk3jzRSFXFz3+SsFM6cmsUHFkylprmDwowUstISCcX4xaWn19Hc0c25xdnsqmpiZ1UzOyqbeGFbFSvWVwCQkhhiUmYKJblpzJqYwbS8NMIh9WSPVUoUQ+zJdQeZnJXCJTPzgw5F4kBpfjr3XD2Xv3vvbDbsr+P3O6pYvbuG5WsP0tbVE3XbpHCIWYUTuGRWPnMnZTJ7UgbzpmSSPyH5rTqnksgSQkZWaiIXzsjjwhl5b5U75zhU38arB+p57UAdL2ytZNWuGl7eeYzkcIhZEzOYMymDMyZmnNJZkYxe+msOocP1bby8s5q/fvdM3Y0tJyUpHHrHB3Nvr6Oiro3q5nbqWrpo6+ohMcFITAiRNyGZwgzvJ5wwct/izYyinDSKctK47pwpzCrMoKO7h91VLbx5tJHtR5vYfKiBkMHsSZkUZiRz+eyCEY1RhocSxRD6qX9afn15ccCRSLwLhYySvDRe2XXshHWVjR1s9ZeDvlchOZzAvCmZzJuSSa9zHKlvZ9Ohel49UM/Hf7Sewoxkri8v4obyEkry0gKNVU6dEsUQ6e11rFh/kItn5FOcq/8QMv6EzJiak8rUnFTeN28ShZnJPLnuIN/9/W6+8/vdLJk/iTsvnc6CkpygQ5WTpEQxRFbtPsah+jbVdwvnAAAQaklEQVQ+f/WcoEOROHKqg+Ej4XRiSwgZNc2dvGfuRM4vzWX1nhp+t72KZzcfZVpeGpfOKuDL180npC7auKBEMUQe/sNectOTeN883WQnEikrNZGr5k/i8tkFrN9Xx6rdx/jx6v2s2n2MT7xrOh9cMJWUxISgw5QolCiGwOo9Nby8o5p/umau/sHLiBrNZyR9JYcTuHhmPhdMz2Pz4Qa2HG7gnqff4D9/u51bLpjGLRdMe8cVWzJ66HKE0+Sc4z+e287EzGQ+cuG0oMMRGfUSQsY5Rdn88q5LeOITizm7KJv/emEnF331Jf7xqY28ebQx6BClD51RnKbfba9iw/467vvgmTqbEDkJZsZFM/K5aEY+u6ub+f6qvTy1oYIV6yu4cHoe15cXseTMSaQl6WMqaOYi79WPU+Xl5W79+vUj/r4d3T0s/dYq2rp6eOFzl5F4GteLx1MXgshwae3sZt3eWtbtr6O2pZP0pATef/ZkPnxeMeeX5mhanCFmZhucc+WD1VOqPg33P7udN4828fBHy08rSYiIJy0pzGWzC7n0jAJmTczgqQ0H+fWmI6xYX8HEzGQuP6OQd88p4OKZ+WSkJAYd7rihRHGKnt9ayaOr9nLbRaW8V1c6iQwpM2NRWS6LynL50nXz+c3mo7ywrZJn3jjCk+sPEg4ZxblplOalMTU7jak5qWSmhN9xxhH0zYhjiRLFKdh3rIW//+lGzpyayT3X6L4JkeGUlhTmQwuL+NDCIrp6enl1fx2/217NL14/xP/uqKbX7z2fkBxmSnYKBROSyZ2QzNScVMry0pmSnaJpRE6TEsVJ2nq4kY8+uhYz+OaNC0kOawBbZKQkJoRYPD2PxdPzKMlNo7O7l6MNbRyq934O17ez91gLXT2OX248DHhXWU3OSmFqdqo/V5V393hRTipF2WlMzk5R1/EglChOwqpdx/jUYxuYkBxm+Z0XUpafHnRIImNWLBd4JIVDlOSlU5L39v9F5xxN7d3UtHRS09xBbUsn9W1dHG1o582jTTS2dRF5CY8BmamJZKclkpOWRE5aEteePZnS/DRK89LJTU8a94PoMSUKM1sCfB1IAL7nnPtqn/XJwI+A84Aa4Abn3D5/3T3AHUAP8Bnn3HPR2jSzMmA5kAu8CnzEOdd5ert5eqoa2/nKs2/y89cOMbNwAj/62CKmZKcGGZKIDMDMyExNJDM1sd8vc929vTS2dVPX2kl9ayd1rV3U+clkX00LGw/W87vtVW/Vz0gJU5qXTml+OqV5af7y+EoigyYKM0sAvg28F6gA1pnZSufc1ohqdwB1zrmZZrYMuB+4wczmAcuA+cAU4AUzO8PfZqA27wcecM4tN7MH/ba/OxQ7ezK6e3pZs7eWn22o4NnNR+npdfz1FTP5y8tn6LpukTgWDoXITU8iNz2p3/Xdvb3Ut3RR09LBseZOalo6qGnuZNWuY/xqY+c7zkb6TyLe8lhKIrF84i0Cdjnn9gCY2XJgKRCZKJYCX/KXnwK+Zd4RWgosd851AHvNbJffHv21aWbbgCuAm/w6P/TbHZZEUdfi/SNoaOuitqWLQ3WtHKxrY/OhBjZVNNDW1UNGcpgPLJjCJy+dQam6mkTGvHAoRH5GMvkZyczus+54EjmzKJN9x1rZV9PC3mPeWcivNx1+a2AdvCQyLS+N/AnJ5KYnkZeeRG56Mtlp3hMLUxMTSE8Ok5qUQFpSAmmJYVKSQoRDIRLMCIW8WEIhSDAjIWQ4B73O0fvWb0dKOGHYJ1eMJVFMBQ5GvK4AFg9UxznXbWYNQJ5fvrrPtlP95f7azAPqnXPd/dQfcp998nVe3lH9jrLkcIg5kzO54fxiFpXlcsWcQt1xLSLA20nkijknXhLf2d3LwbpW9te0sPeY9/tAbSs1zZ3srGympqWD9q7eIY/phc9dxszCCUPebqRYEkV/qarv7dwD1RmovL9LDKLVPzEoszuBO/2XzWa2vb96p2IHsHKoGotdPnDiU2rGNx2T/um4nGhEj8nNI/VGMZh1/4CrYjkmMU1QF0uiqAAiH9lWBBweoE6FmYWBLKB2kG37Kz8GZJtZ2D+r6O+9AHDOPQQ8FEP8ccHM1sdyK/14omPSPx2XE+mYnGgoj0ksFw+vA2aZWZmZJeENTvf9wr0SuNVf/jDwkvMmkVoJLDOzZP9qplnA2oHa9Lf5nd8Gfpu/OPXdExGR0zXoGYU/5nAX8BzepayPOue2mNm9wHrn3ErgEeDH/mB1Ld4HP369FXgD393Ap51zPQD9tem/5eeB5Wb2r8BrftsiIhKQMTF77FhgZnf63Wni0zHpn47LiXRMTjSUx0SJQkREotIEJyIiEpUSRcDMbImZbTezXWZ2d9DxDDcze9TMqsxsc0RZrpk9b2Y7/d85frmZ2Tf8Y7PJzBZGbHOrX3+nmd3a33vFCzMrNrPfmdk2M9tiZp/1y8ftcTGzFDNba2Yb/WPyZb+8zMzW+Pv3pH8xDP4FM0/6x2SNmZVGtHWPX77dzK4KZo+GjpklmNlrZvYr//XwHxPnnH4C+sEbyN8NTAeSgI3AvKDjGuZ9vhRYCGyOKPt34G5/+W7gfn/5GuBZvPtrLgDW+OW5wB7/d46/nBP0vp3GMZkMLPSXM/Bu5Zk3no+Lv28T/OVEYI2/ryuAZX75g8Bf+st/BTzoLy8DnvSX5/n/r5KBMv//W0LQ+3eax+ZzwBPAr/zXw35MdEYRrLemR3HexIfHp0cZs5xzL+NdGRdpKd50Lfi/PxBR/iPnWY13j81k4CrgeedcrXOuDngeWDL80Q8P59wR59yr/nITsA1vRoJxe1z8fWv2Xyb6Pw5vip+n/PK+x+T4sXoKuLLvNELOub1A5DRCccfMioD3A9/zXxsjcEyUKILV3/QowzZlySg20Tl3BLwPTaDQLx/o+IzZ4+Z3DyzA+wY9ro+L38XyOlCFl/R2M/AUP++YRgiInEZozBwT4L+AfwSOzwUSbdqjITsmShTBinnKknHqZKeGiWtmNgH4GfA3zrnGaFX7KRtzx8U51+OcOxdvhoZFwNz+qvm/x/wxMbNrgSrn3IbI4n6qDvkxUaIIVizTo4wHlX7XCf7v4w8DGOj4jLnjZmaJeEnicefc037xuD8uAM65euD3eGMU2eZNEwTv3L+39t1in0Yo3lwMXGdm+/C6qa/AO8MY9mOiRBGsWKZHGQ8ip4CJnLZlJfBR/yqfC4AGvwvmOeB9ZpbjXwn0Pr8sLvn9xo8A25xzX4tYNW6Pi5kVmFm2v5wKvAdv7GagKX5OdhqhuOOcu8c5V+ScK8X7rHjJOXczI3FMgh7BH+8/eFew7MDrf/2noOMZgf39CXAE6ML7ZnMHXr/pi8BO/3euX9fwHnC1G3gDKI9o52N4g3C7gNuD3q/TPCaX4J36bwJe93+uGc/HBTgbbwqfTcBm4It++XT/Q20X8FMg2S9P8V/v8tdPj2jrn/xjtR24Ouh9G6LjczlvX/U07MdEd2aLiEhU6noSEZGolChERCQqJQoREYlKiUJERKJSohARkaiUKCRumdltZvbKMLT7JTN7bKjbjeF9L/ZnAG02sw8MvsUJ2z9oZl/wly83s4ohji+Q4yLBG/RRqCIyYu4FvuWc+3p/K/07cicCPXj3ofwR+JRz7vh8Pp8aoThlnNEZhcjoMQ3YMkidP3POTcCbmrwS+OawRzUMIqackDigRCEnxX/IztNmVm1mNWb2Lb98hpm95JcdM7PHj0/B4K//vJkdMrMm/2EpV/rlPzCzf42o944uEzO728x2+9ttNbMPxhjnb8zsrj5lG83sQ/7y183soJk1mtkGM3vXAO2c0IVjZvvM7D3+cigixhozW2FmuVHi+oT/wJhaM1tpZlP88uPPJfml3/WUHG3/nHPteFNHz4to+x3Hss/7fsY/fkX+62vN7HUzqzezP5rZ2RF1+/1b+ZLM7Ef+ui1mVh6x3YB/K7+bcJWZPWBmtcCX/PKPmffApjoze87MpkXbbwmGEoXEzMwSgF8B+4FSvKmJlx9fDXwFmII3y2cxb38YzAbuAs53zmXgPTdhX4xvuxt4F96EZl8GHjN/orxBPAHcGBH7PLxv7L/2i9YB5+I95OcJ4KdmlhJjTJE+gzf//2V4+16HN73GCczsCrxj9Bd4ZwT78Y+fc24GcAD/jME51xHtTc0sDbgBWD1YgP64xW3AZc65CvOeiPco8Em8aUL+G1jpz/0z2N/qOj/mbLw5g74VsW6wv9VivIcpFQL3+eMw/wf4EFAA/AFvihcZbYKes0Q/8fMDXAhUA+EY6n4AeM1fnok38+l7gMQ+9X4A/GvE68uBiijtvg4s9ZdvA14ZoF4G0AJM81/fBzwapd064Bx/+UvAYwPFg/fB+R5/eRtwZcS6yXjjByccI7yJ//494vUEv25p33YHiHEf0AzUA914M36e1d+x9OM+BHwNeAXIiqj3XeBf+rS9HS/ZRftbfQl4IeL1PKDtJP5WB/qsfxa4I+J1CGg9/jfTz+j50RmFnIxiYL97+yEpbzGzQjNb7ndZNAKPAfkAzrldwN/gfdBU+fWmxPKGZvbRiC6SeuDM4+1G47wnxf0ab5ZN/N+PR7T7d36XR4PfblYs7fZjGvDziPi24Q02T+yn7hS8s4jjMTYDNZzcg3Q+4JzLxnuM5V3A/5rZpAHqZgN3Al9xzjX0ifnvjsfsx10MTInhb3U0YrkVSDk+3hDD3yryYTnH4/h6RP1avDPTeH6w0JikRCEn4yBQYv0PRH4FbwbUs51zmcAtRDwgxTn3hHPuErwPBwfc769qAdIi2nnrQ8/vr34Y7wMxz/+A3BzZ7iB+AtxoZhcCqXjTMeOPR3werwsox2+3YYB23xGf3/1WELH+IN7sm9kRPynOuUP9tHXY3//jbaXjdf30Vzcq5z3U52m8pHTJANXqgGuB75vZxX1ivq9PzGnOuZ/4bQ/0txpQjH+rvjOQHgQ+2SeOVOfcHwd7PxlZShRyMtbiTRH+VTNLN7OUiA+gDPxuETObCvzD8Y3MbLaZXeEP0LYDbXgfcOBPqW1muf4347+JeL90vA+Xar+d2/G+pcbqGbwPu3vxHix//PGRGXhdN9VA2My+CGQO0MYOvG/N7zfv4UL/F+/b/HEP4vW3T/NjLDCzgZ57/gRwu5md6x+LfwPWOOf2ncQ+4b+P+e+Tg3cW0y/n3O+Bm/HOehb7xQ8DnzKzxX476f7+ZQzyt4rmVP5WDwL3mNl8f5ssM7s+hveSEaZEITFzzvUAf4bXj30A73kSN/irvwwsxPtm/mvg6YhNk4GvAsfwui4K8QYxAX4MbMTrf/8t8GTE+20F/hP4E96loGcBq04i3g4/jvfgfUgf9xxe//gOvK6gdk7sFjneRgPwV3gPsz+Ed4YReRXU1/EGdX9rZk14g8uL+7bjt/Ui8AW8J9kdAWbwdtdYrH5pZs1AI964y63OuaiX1DrnngduxxuwPs85tx74BN5AdB3e8wpu86tH+1tFe4+T/ls5536Od7ay3O+u3AxcPdh7ycjT8yhERCQqnVGIiEhUShQiIhKVEoWIiESlRCEiIlEpUYiISFRKFCIiEpUShYiIRKVEISIiUSlRiIhIVP8fYgGY4qQsEeUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xda59f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 目标y casual的直方图／分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.casual.values, bins=30, kde=True)\n",
    "plt.xlabel('casual value of Bikeshare', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xda59e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "plt.scatter(range(data.shape[0]), data[\"casual\"].values,color='purple')\n",
    "plt.title(\"Distribution of casual\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 两两特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe7b5e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get the names of all the columns\n",
    "cols=data.columns \n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_corr = data.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练数据\n",
    "\n",
    "- 下面只通过分析‘casual’为例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(584, 11)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = data['casual'].values\n",
    "X = data.drop(columns=['instant','dteday','casual','registered','cnt'])\n",
    "\n",
    "#用于后续显示权重系数对应的特征\n",
    "columns = X.columns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.2)\n",
    "X_train.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import MaxAbsScaler\n",
    "\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "ss_X = MaxAbsScaler()\n",
    "ss_y = MaxAbsScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)\n",
    "\n",
    "#对y做标准化不是必须\n",
    "#对y标准化的好处是不同问题的w差异不太大，同时正则参数的范围也有限\n",
    "y_train = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test = ss_y.transform(y_test.reshape(-1, 1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 缺省参数的线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[0.39312304943885606]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[0.14075217946833887]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[0.08923135504383013]</td>\n",
       "      <td>yr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0.07888997400147321]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[0.036045892778650154]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[-0.06506959349197622]</td>\n",
       "      <td>mnth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[-0.08295693886648647]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[-0.09276889179643774]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[-0.11045733041986946]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[-0.1290209513257763]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[-0.24640927577485547]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      coef     columns\n",
       "7    [0.39312304943885606]        temp\n",
       "8    [0.14075217946833887]       atemp\n",
       "1    [0.08923135504383013]          yr\n",
       "0    [0.07888997400147321]      season\n",
       "4   [0.036045892778650154]     weekday\n",
       "2   [-0.06506959349197622]        mnth\n",
       "3   [-0.08295693886648647]     holiday\n",
       "9   [-0.09276889179643774]         hum\n",
       "10  [-0.11045733041986946]   windspeed\n",
       "6    [-0.1290209513257763]  weathersit\n",
       "5   [-0.24640927577485547]  workingday"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 线性回归\n",
    "#class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 使用默认配置初始化\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.7223830035667711\n",
      "The r2 score of LinearRegression on train is 0.6766530446934129\n"
     ]
    }
   ],
   "source": [
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print 'The r2 score of LinearRegression on test is', r2_score(y_test, y_test_pred_lr)\n",
    "#训练集\n",
    "print 'The r2 score of LinearRegression on train is', r2_score(y_train, y_train_pred_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IRz7CHnvsQVNTE1/60pe22ce4ceNoaWlh+vTpjBs3jmOOOWbrJKyLLrqI2bNnM23aNGbOnLm1J3rOOefw1re+lebmZqZMmcJrX/val9W2ePFirrjiCpqamhg2bFiHPelZs2ZtvW729OnTed/73tfp6540aRKf/vSnefOb38xLL71EU1MTV199NccffzxTp07liCOO4JBDDuGEE05ovDG7ITKzTw4E0NzcnK0z/qSBzJ60trd8+XJe97rX9XcZ6mHtva8RsTQzmzvYZBuek5YkqVCGtCRJhTKkJakQfXn6Ub2vJ97PhkI6IkZGxK0R8ZOIWB4R0yNiVETcGREr6ttXdbsaSRqghg4dyoYNGwzq3UTr70kPHTq0W/tpdHb3F4B/zsyzImJP4JXAx4BFmXl5RFwCXAJ8tFvVSNIANX78eFatWsX69ev7uxT1kKFDhzJ+/Phu7aPTkI6I4cBJwAUAmfki8GJEzAZm1KvNBxZjSEtSlzQ1NTFx4sT+LkOFaWS4+xBgPfD1iPhxRHwlIvYCxmbmGoD6dkx7G0fEvIhYEhFL/IQoSVLjGgnpwcAxwJcycyrwHNXQdkMy85rMbM7M5tbrt0qSpM41EtKrgFWZeW/9+Faq0F4bEeMA6tt1vVOiJEkDU6chnZm/Ah6NiMPrRTOBB4HbgTn1sjnAy3/0U5IkdVmjs7v/ALihntn9EPD7VAF/c0TMBX4JvK13SpQkaWBqKKQzcxnQ3nVGZ/ZsOZIkqZVXHJMkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQjV4WVFI/aGnp2fUk7VrsSUuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKtTgRlaKiIeBZ4EtwObMbI6IUcACYALwMPD2zHyyd8qUJGng2Zme9CmZOSUzm+vHlwCLMvNQYFH9WJIk9ZDuDHfPBubX9+cDZ3a/HEmS1KrRkE7gjohYGhHz6mVjM3MNQH07pr0NI2JeRCyJiCXr16/vfsWSJA0QDZ2TBk7IzMciYgxwZ0T8pNEDZOY1wDUAzc3N2YUaJUkakBrqSWfmY/XtOuA2YBqwNiLGAdS363qrSEmSBqJOQzoi9oqIvVvvA28G7gduB+bUq80BFvZWkZIkDUSNDHePBW6LiNb1b8zMf46I/wRujoi5wC+Bt/VemZIkDTydhnRmPgRMbmf5BmBmbxQllaSlpb8rkDRQecUxSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFWpwfxcg9bSWlv6uQJJ6hj1pSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCNRzSETEoIn4cEf9YP54YEfdGxIqIWBARe/ZemZIkDTw705P+ALC8zePPAp/PzEOBJ4G5PVmYJEkDXUMhHRHjgVnAV+rHAbwRuLVeZT5wZm8UKEnSQNXoT1X+DfAnwN71432ApzJzc/14FXBAextGxDxgHsBBBx3U9UoldWhnfp7Tn/KUdh2d9qQj4nRgXWYubbu4nVWzve0z85rMbM7M5tGjR3exTEmSBp5GetInAGdExO8CQ4HhVD3rkRExuO5Njwce670yJUkaeDrtSWfmpZk5PjMnAO8A/i0zzwHuAs6qV5sDLOy1KiVJGoC68z3pjwIfioiVVOeov9ozJUmSJGh84hgAmbkYWFzffwiY1vMlSZIk8IpjkiQVy5CWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKtTg/i5AUt9qaenZ9ST1HnvSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUJ2GdEQMjYgfRsR/RcQDEfGpevnEiLg3IlZExIKI2LP3y5UkaeBopCf9G+CNmTkZmAK8JSKOBz4LfD4zDwWeBOb2XpmSJA08nYZ0VjbWD5vqfwm8Ebi1Xj4fOLNXKpQkaYBq6Jx0RAyKiGXAOuBO4OfAU5m5uV5lFXBA75QoSdLA1FBIZ+aWzJwCjAemAa9rb7X2to2IeRGxJCKWrF+/vuuVSpI0wOzU7O7MfApYDBwPjIyI1t+jHg881sE212Rmc2Y2jx49uju1SpI0oDQyu3t0RIys778CeBOwHLgLOKtebQ6wsLeKlCRpIBrc+SqMA+ZHxCCqUL85M/8xIh4EboqITwM/Br7ai3VKkjTgdBrSmXkfMLWd5Q9RnZ+WJEm9wCuOSZJUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhRrc3wVIjWpp6e8KJKlv2ZOWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqLmahfeYGScjX63vgeSr3HnrQkSYXqNKQj4sCIuCsilkfEAxHxgXr5qIi4MyJW1Lev6v1yJUkaOBrpSW8G/jgzXwccD7wvIiYBlwCLMvNQYFH9WJIk9ZBOQzoz12Tmj+r7zwLLgQOA2cD8erX5wJm9VaQkSQPRTk0ci4gJwFTgXmBsZq6BKsgjYkwH28wD5gEcdNBB3alVUoGcYCb1noYnjkXEMOD/AH+Umc80ul1mXpOZzZnZPHr06K7UKEnSgNRQSEdEE1VA35CZ36oXr42IcfXz44B1vVOiJEkDUyOzuwP4KrA8Mz/X5qnbgTn1/TnAwp4vT5KkgauRc9InAOcB/x0Ry+plHwMuB26OiLnAL4G39U6JkiQNTJ2GdGZ+H4gOnp7Zs+VIkqRWXnFMkqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQu3UT1VK/iyhJPUde9KSJBXKkJYkqVCGtCRJhTKkJUkqlBPH1CucOCZJ3WdPWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgo1uL8LUO9paenvCiRJ3WFPWpKkQnUa0hHxtYhYFxH3t1k2KiLujIgV9e2rerdMSZIGnkZ60tcCb9lu2SXAosw8FFhUP5YkST2o05DOzLuBJ7ZbPBuYX9+fD5zZw3VJkjTgdfWc9NjMXANQ347paMWImBcRSyJiyfr167t4OEmSBp5enziWmddkZnNmNo8ePbq3DydJ0m6jqyG9NiLGAdS363quJEmSBF0P6duBOfX9OcDCnilHkiS1auQrWN8E/gM4PCJWRcRc4HLg1IhYAZxaP5YkST2o0yuOZeY7O3hqZg/XIkmS2vCKY5IkFcqQliSpUIa0JEmFMqQlSSqUP1UpqU/szE+n7go/s9pojbvCa1G57ElLklQoQ1qSpEIZ0pIkFcqQliSpUE4ck7TLcvKWdnf2pCVJKpQhLUlSoQxpSZIKZUhLklQoJ44VxEkwUsX/41LFnrQkSYUypCVJKpQhLUlSoTwnLUm1/jwXvrv9Sph6hj1pSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCuUVx3ZBXm1I2jm7ypXEpO3Zk5YkqVCGtCRJhTKkJUkqlCEtSVKhdumJY/60m6SBqNG/Z/7d27FdoR3tSUuSVChDWpKkQhnSkiQVypCWJKlQu/TEsZ3RXyf+nbghqb/09MSoXeHKbbvb31x70pIkFapbIR0Rb4mIn0bEyoi4pKeKkiRJ3QjpiBgEXA38DjAJeGdETOqpwiRJGui605OeBqzMzIcy80XgJmB2z5QlSZIiM7u2YcRZwFsy81314/OA4zLz/dutNw+YVz88HPhpm6f3BR7vUgHqLtu+/9j2/ce27z+2/W8dnJmjG1mxO7O7o51lL0v8zLwGuKbdHUQsyczmbtSgLrLt+49t339s+/5j23dNd4a7VwEHtnk8Hnise+VIkqRW3Qnp/wQOjYiJEbEn8A7g9p4pS5IkdXm4OzM3R8T7gX8BBgFfy8wHdnI37Q6Dq0/Y9v3Htu8/tn3/se27oMsTxyRJUu/yimOSJBXKkJYkqVB9GtIRMSoi7oyIFfXtq3aw7vCIWB0RV/VljburRto+IqZExH9ExAMRcV9EnN0fte4uOrtsbkQMiYgF9fP3RsSEvq9y99RA238oIh6s/58vioiD+6PO3VGjl4uOiLMiIiPCr2XtQF/3pC8BFmXmocCi+nFH/hz4bp9UNTA00vbPA+dn5hHAW4C/iYiRfVjjbqPBy+bOBZ7MzNcAnwc+27dV7p4abPsfA82ZeTRwK/BXfVvl7qnRy0VHxN7AHwL39m2Fu56+DunZwPz6/nzgzPZWiojXA2OBO/qoroGg07bPzJ9l5or6/mPAOqChq+LoZRq5bG7b9+RWYGZEtHeRIO2cTts+M+/KzOfrhz+gus6Duq/Ry0X/OdUHoxf6srhdUV+H9NjMXANQ347ZfoWI2AP4a+AjfVzb7q7Ttm8rIqYBewI/74PadkcHAI+2ebyqXtbuOpm5GXga2KdPqtu9NdL2bc0FvtOrFQ0cnbZ9REwFDszMf+zLwnZV3bksaLsi4l+B/dp56uMN7uK9wD9l5qN2KnZOD7R9637GAd8A5mTmSz1R2wDUyGVzG7q0rnZaw+0aEecCzcDJvVrRwLHDtq87YZ8HLuirgnZ1PR7Smfmmjp6LiLURMS4z19RBsK6d1aYDJ0bEe4FhwJ4RsTEz/b3qTvRA2xMRw4FvA3+amT/opVIHgkYum9u6zqqIGAyMAJ7om/J2aw1dsjgi3kT1AfbkzPxNH9W2u+us7fcGjgQW152w/YDbI+KMzFzSZ1XuQvp6uPt2YE59fw6wcPsVMvOczDwoMycAHwauM6B7RKdtX1/e9TaqNr+lD2vbHTVy2dy278lZwL+lVxfqCZ22fT3k+mXgjMxs9wOrumSHbZ+ZT2fmvpk5of4b/wOq98CA7kBfh/TlwKkRsQI4tX5MRDRHxFf6uJaBppG2fztwEnBBRCyr/03pn3J3bfU55tbL5i4Hbs7MByLisog4o17tq8A+EbES+BA7/raDGtRg219BNVJ3S/3/3N8d6AENtr12gpcFlSSpUF5xTJKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIK9f8Bm9Wj0wzGk20AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeda80f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - y_train_pred_lr,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Bikeshare casual of Residuals\") \n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xed97438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_train, y_train_pred_lr)\n",
    "plt.plot([0, 1], [0, 1], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.axis('tight')\n",
    "plt.xlabel('True casual')\n",
    "plt.ylabel('Predicted casual')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 正则化的线性回归（L2正则 --> 岭回归）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.722168443611797\n",
      "The r2 score of RidgeCV on train is 0.676232476079685\n"
     ]
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "#n_alphas = 20\n",
    "#alphas = np.logspace(-5,2,n_alphas)\n",
    "\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#模型训练\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "#预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge)\n",
    "print 'The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121f6e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 1.0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[0.39312304943885606]</td>\n",
       "      <td>[0.2973303296012433]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[0.14075217946833887]</td>\n",
       "      <td>[0.23444038818865615]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[0.08923135504383013]</td>\n",
       "      <td>[0.08904159905925796]</td>\n",
       "      <td>yr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0.07888997400147321]</td>\n",
       "      <td>[0.07324491070274175]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[0.036045892778650154]</td>\n",
       "      <td>[0.03610264443533151]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[-0.06506959349197622]</td>\n",
       "      <td>[-0.05789503893723019]</td>\n",
       "      <td>mnth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[-0.08295693886648647]</td>\n",
       "      <td>[-0.07678748080559669]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[-0.09276889179643774]</td>\n",
       "      <td>[-0.08389506552954057]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[-0.11045733041986946]</td>\n",
       "      <td>[-0.09954663133921565]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[-0.1290209513257763]</td>\n",
       "      <td>[-0.1286602209786482]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[-0.24640927577485547]</td>\n",
       "      <td>[-0.24336718557419734]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   coef_lr              coef_ridge     columns\n",
       "7    [0.39312304943885606]    [0.2973303296012433]        temp\n",
       "8    [0.14075217946833887]   [0.23444038818865615]       atemp\n",
       "1    [0.08923135504383013]   [0.08904159905925796]          yr\n",
       "0    [0.07888997400147321]   [0.07324491070274175]      season\n",
       "4   [0.036045892778650154]   [0.03610264443533151]     weekday\n",
       "2   [-0.06506959349197622]  [-0.05789503893723019]        mnth\n",
       "3   [-0.08295693886648647]  [-0.07678748080559669]     holiday\n",
       "9   [-0.09276889179643774]  [-0.08389506552954057]         hum\n",
       "10  [-0.11045733041986946]  [-0.09954663133921565]   windspeed\n",
       "6    [-0.1290209513257763]   [-0.1286602209786482]  weathersit\n",
       "5   [-0.24640927577485547]  [-0.24336718557419734]  workingday"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "#这是为了标出最佳参数的位置，不是必须\n",
    "#plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12db11d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - y_train_pred_ridge,bins=40, label='Ridge Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Bikeshare casual of Ridge\") \n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeddab00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_train, y_train_pred_ridge)\n",
    "plt.plot([0, 1], [0, 1], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.axis('tight')\n",
    "plt.xlabel('True casual')\n",
    "plt.ylabel('Predicted casual')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 正则化的线性回归（L1正则 --> Lasso）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.7214038524606173\n",
      "The r2 score of LassoCV on train is 0.6765410739926949\n"
     ]
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#设置超参数搜索范围\n",
    "#alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "#生成一个LassoCV实例\n",
    "#lasso = LassoCV(alphas=alphas)  \n",
    "lasso = LassoCV()  \n",
    "\n",
    "#训练（内含CV）\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "#测试\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso)\n",
    "print 'The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12866940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 0.00011830862931073816)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lasso</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.403259</td>\n",
       "      <td>[0.39312304943885606]</td>\n",
       "      <td>[0.2973303296012433]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.127940</td>\n",
       "      <td>[0.14075217946833887]</td>\n",
       "      <td>[0.23444038818865615]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.088936</td>\n",
       "      <td>[0.08923135504383013]</td>\n",
       "      <td>[0.08904159905925796]</td>\n",
       "      <td>yr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.069863</td>\n",
       "      <td>[0.07888997400147321]</td>\n",
       "      <td>[0.07324491070274175]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.034974</td>\n",
       "      <td>[0.036045892778650154]</td>\n",
       "      <td>[0.03610264443533151]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.056204</td>\n",
       "      <td>[-0.06506959349197622]</td>\n",
       "      <td>[-0.05789503893723019]</td>\n",
       "      <td>mnth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.078683</td>\n",
       "      <td>[-0.08295693886648647]</td>\n",
       "      <td>[-0.07678748080559669]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.085319</td>\n",
       "      <td>[-0.09276889179643774]</td>\n",
       "      <td>[-0.08389506552954057]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.104535</td>\n",
       "      <td>[-0.11045733041986946]</td>\n",
       "      <td>[-0.09954663133921565]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.129619</td>\n",
       "      <td>[-0.1290209513257763]</td>\n",
       "      <td>[-0.1286602209786482]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.245256</td>\n",
       "      <td>[-0.24640927577485547]</td>\n",
       "      <td>[-0.24336718557419734]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    coef_lasso                 coef_lr              coef_ridge     columns\n",
       "7     0.403259   [0.39312304943885606]    [0.2973303296012433]        temp\n",
       "8     0.127940   [0.14075217946833887]   [0.23444038818865615]       atemp\n",
       "1     0.088936   [0.08923135504383013]   [0.08904159905925796]          yr\n",
       "0     0.069863   [0.07888997400147321]   [0.07324491070274175]      season\n",
       "4     0.034974  [0.036045892778650154]   [0.03610264443533151]     weekday\n",
       "2    -0.056204  [-0.06506959349197622]  [-0.05789503893723019]        mnth\n",
       "3    -0.078683  [-0.08295693886648647]  [-0.07678748080559669]     holiday\n",
       "9    -0.085319  [-0.09276889179643774]  [-0.08389506552954057]         hum\n",
       "10   -0.104535  [-0.11045733041986946]  [-0.09954663133921565]   windspeed\n",
       "6    -0.129619   [-0.1290209513257763]   [-0.1286602209786482]  weathersit\n",
       "5    -0.245256  [-0.24640927577485547]  [-0.24336718557419734]  workingday"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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